Modern warfare systems have increased in complexity in response to a progressively more multifaceted,unpredictable, and dangerous world. In particular, ground and aerial automated systems have changed thetenor of the battlefield. Robotic systems are becoming an essential part of the Army’s force. They areintended to extendmanned capabilities, be force multipliers, and most importantly, save lives; however, theaddition of robotic systems will likely increase, or certainly change, the Soldier’s cognitive workload.Automation is a possible solution to this cognitive workload

issue. We propose the use of adaptive systemsthat use flexible automation strategies to account for the ever changing combat environment. This paperpresents supporting research, results from two multitasking studies in human robot interaction, and ourrationale for the implementation of adaptive automation in this environment. Finally, we discuss ongoingresearch in terms of its theoretical and Soldier performance implications for designing adaptive algorithms aspart of the crew interface for remote targeting with robotic systems.

1.0

INRODUCTION

Modern warfare systems have increased in complexity (i.e., enhanced capabilities and broader operatingrequirements) in response to the increasingly unpredictable and dangerous world circumstances. Ground and

aerial automated systems have become salient elements on the battlefield and thus, have been part of thechanging tenor of the battlefield. With automated systems, Soldiers can be out of harm’s way and will be ableto access portions of the battlespace by

proxy. Using robotic systems, they will be able to venture into areasof the battlespace previously unavailable, effectively increasing their firepower and multiplying theirintelligence gathering capabilities. However, technology has its price. Despite

the prevalent use of the term“unmanned” to describe robotic systems, the implied assumption that such systems are fully autonomous isnot the case: manning requirements will remain to perform robotic system supervision and control tasks.Robotic systems

will increase, or change, rather than decrease Soldiers’ cognitive workload on the battlefield.The Soldier will have to perform driving, security, reconnaissance, and combat tasks while coordinating andsome cases controlling multiple robotic systems. This multitasking environment will not only make it difficultto conduct robotic tasks but will also decrease the Soldier’s cognitive capacity needed to attend to theimmediate environment [1, 2, 18].

A possible solution to the cognitive workload issue is to automate segments of the Soldier’s tasks; however,automation decisions are difficult because of the volatility and unpredictability of the battlefield environment.As the environment changes, optimal automation strategies must change as well. As a result,static

automationAdaptive Automation for Robotic Systems

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put in place at the system design phase may not be sufficiently robust to contextual changes in theenvironment. We are proposing the use of adaptive systems to employ flexible automation strategies as afunction of the changing environment and, importantly, the changing operator state as well. Adaptiveautomation refers to an automation design concept in which task allocation and coordination between humanoperators and automated systems are flexible and context dependent [15, 22, 23,29]. In this paper we outlinethe motivations behind our development program of adaptive automation for robotic systems, explicate thetheoretical issues that drive it, compare various adaptive options, and discuss preliminary results that will beused toestablish performance guidelines for implementing adaptive automation as a mitigation strategy.

2.0

MULTITASKING AND MULTIPLE ROBOTIC SYSTEMS IN COMBATENVIRONMENTS

In the future, the military force will include a variety of unmanned systems, ranging fromsmall teleoperatedground robots and utility vehicles for logistics support and casualty extraction to 6-ton vehicular robots withgun systems and smaller unmanned aerial vehicles (UAVs). Integrating this “team” of robotic systems withthe Soldier teams will be a key component of their tactical deployment. The systems are expected to be mostuseful when they are used synergistically. For example, a UAV has a panoramic view of the battlespacewhereas a small unmanned ground vehicle (SUGV) can view spaces hidden from both the operator and theUAV. Armed robotic vehicles (ARVs) can augment both systems by providing defense with its automatedweapons and anti-tank capacity. Our experimental focus is on control of the approximately 6-8 ton ARV and aClass I or Class II UAV, all of which can be controlled either from a dismounted position or from an armoredvehicle. We are assuming that operators are heavily loaded with these robotic control or management tasksincluding the communications necessary to coordinateactivities. Additionally, a critical consideration will beensuring local security, which requires continuous awareness of the threats to the operator’s own vehicle.Mitchell [18] modeled the cognitive workload involved for mounted control of ARVs and concluded that thegunner/ARV operator would be heavily loaded when local security was a priority in addition to the ARVtasks. Operators would be engaged in using the ARV for remote targeting when they were most vulnerable tothreats in their own vehicle’s immediate environment. Adaptively automating some of the gunner’s tasks mayalleviate the multitasking workload problem.

2.1

Supporting Research

There are a number of human-robot interactions issues that need to be addressed to allow the operators toconduct their mission while using multiple robotic systems. An often repeated goal is that one operator willcontrol/monitor multiple robotic assets. To explore this issue, Rehfeld and colleagues at the University ofCentral Florida created a scale model replica of an Iraqi urban area with multiple roads, buildings, vehiclesand crowd scenes [28]. The general task was to monitor a robot’s progress via a video feed sent from therobotic vehicle and report on specific pre-briefed targets in the urban scenes. Different combinations ofnumber of operators and number of robots were examined. The Reserved Officer Training Corps (ROTC)participants had a difficult time reporting the military targets; even the more senior ROTC students found itdifficult. More surprising, the ideal ratio was two operators to one vehicle; the participants in this conditionfound nearly 200% more targets than a single operator and teams always performed more effectively thansingle operators. Note, control of the robot was not an issue since the operator simply planned the robot routesthrough the urban streets and then the robots were automatically “driven” on the planned route. In general,affording the operators a second vehicle did not improve performance [28]. Results suggest that withoneoperator it is difficult to interpret all the information received by the robot. By having additional operators,target identification performance was improved; multiple operators were viewing, sharing information, andAdaptive Automation for Robotic Systems

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interpreting the same video feedfrom the robot. However, when the ratio was two operators to two robots,performance was no better than the one operator to one robot condition. Each operator chose a robot tomonitor and in essence the task was completed the same as the one operator to one robot condition. Thisfinding also reaffirmed real world data collected during rescue missions for the victims in the collapsed WorldTrade

Center [20]. The key factor in the successful rescue mission was understanding the video informationreceived from

the rescue robots concerning locations of possible victims. Thus, as found with

Rehfeld

et al.’sresearch, the more operators involved, the more information that can be gathered about a situation using asingle robotic asset.

Chen, Durlach, Sloan and Bowens [5] obtained similar findings in a simulation study on controlling single ormultiple unmanned assets (i.e., UGV, UAV) from a mounted armored vehicle interface. Participants usedeither one (UGV or UAV) or three (mixed) unmanned assets to complete a remote targeting task. In additionto number of assets available, Chen et al. manipulated the control modality for only the UGV asset; theparticipants controlled the UGV by

teleoperating

it with a joystick or waypoint control. Not surprisingly,when the UGV was the only asset, the teleoperation was less effective than waypoint control. The mixedcondition showed a pattern similar to the Rehfeld et al. [28] study. Participants were not effective at usingmultiple assets and having additional assets for targeting resulted in only minimal improvements in targeting(Figure 1). Even in the mixed condition, the preferred tactic was to rely on the UAV alone rather than toattempt an integrated strategy with the UGV. Both studies are worrisome. They indicate thateven if roboticassets have minimal control requirements (pre-mission waypoint control), remote targeting is a difficult taskfor the operator. What is more, neither study factored in the realistic multitasking requirements for operatorsor the stress ofcombat, in which case performance would likely be worse. Based on Mitchell’s [18]

modeling

results, Chen and her colleagues recently completed a second study in which she investigated having thegunner/ARV operator perform remote targeting with a UGV and UAV using more realistic multitaskingenvironments. The findings showed that robotic control and targeting are difficult and that mitigationstrategies such as automation need to be developed if robotic operations can be accomplished with a singleoperatorin multitasking environments.

Figure 1. Mean Targets Lased As a Function of Control Modality and Number of Assets

2.2

Adaptive Automation

Both for engineering and future army force efficiency, there is a premium on reducing crew size for armoredvehicles. A possible solution is to automate as many operator tasks and functions as possible; however,UAV

UGV

Waypoint Controlled

UGV

Teleoperated

Mean Targets Lased

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possible; research indicates that automation introduces its own problems. Improperly implementedautomation can have disastrous consequences and the literature is replete with examples of misuse and disuseof automated systems [10, 19, 26]. In particular, complacency effects, misusing systems, and performancedecrements because of the operator being out of the loop, all suggest that automation should beimplementedcarefully to ensure that operators maintain an awareness of the battlespace and their robotic assets and do notoverly, or improperly rely on the automated systems [8, 11, 25, 30]. Supporting a high level of awareness iscrucial in a battlefield environment.

Adaptive automation has been proposed as a mitigating strategy to minimize the potential human performancecosts of automation [23]. Adaptive automation uses mitigation criteria that drive an invocation mechanism or“trigger” to maintainan effective mixture of operator engagement and automation for a dynamic multitaskenvironment (Figure 2). The invocation mechanism is triggered by whatever measurement process is used torepresent the current state of the operator and/or task. If properly instrumented the results of the measurementprocess should be displayed to operators in order to keep them informed of the state of the invocation process.

To be effective, the invocation process must be sensitive to the operator’s combined tasking environment,which depends on interactions among tasks, the environment and the operator state (e.g., workload).

Figure 2. Example of closed loop adaptation for A-automated, A/M–

automated / manual, and M-

manualtask sets

The effects of adaptiveautomation have been examined in a number of studies [13, 14, 16, 22, 32]. Thesestudies have shown that, compared to static or non-adaptive automation, adaptive automation can reduce oreliminate some of the human performance costs of automation, including unbalanced workload [15],complacency and reduced situation awareness [25], and cognitive skill loss [12]. However, with a fewexceptions [e.g., 24] adaptive automation has been examined in the context of aviation and process control,not human-robot interaction. Accordingly, there is a need to examine its efficacy for the specific problemsfaced by human operators supervising multiple robotic systems.

The method of invocation is an important issue in adaptive automation. There are four major invocation

methods for automation [23]. In the critical-events method, automation is invoked only when certain tacticalenvironmental events occur [1]. For example, in an aircraft air

defense

system, the beginning of a "pop-up"weapon delivery sequence leads to theautomation of all defensive measures of the aircraft. If the criticalevents do not occur, the automation is not invoked. This method is tied to current tactics and doctrine

Operator

Environmental

Forcing functionsfunctions

A

A/M

A/M

M

M

M

Invocationmechanism

Measurementof task effects

Multi-task state

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established during mission planning. A disadvantage of the method is its possible

insensitivity to dynamicreal-time system and human operator performance. The critical-events method will invoke automationirrespective of whether or not the pilot requires it when the critical event occurs. One potential way toovercome this limitation

is to measure operator performance and physiological activity [23]. In the operatorperformance measurement and operator physiological assessment method the operator mental states (e.g.,mental workload, or more ambitiously, operator intentions) may be inferred from performance or othermeasures. The measures are used as inputs for the adaptive logic [e.g. 4, 27] The Defense Advanced ResearchProjects Agency Augmented Cognition program is currently attempting to validate the use of suchphysiological techniques for real-time adaptive automation based on assessment of operator states [34]. In thehuman operator

modeling

method, the operator states and performance are

modeled

theoretically and theadaptive algorithm is driven by the model parameters. The hybrid method combines one or more of thesedifferent invocation techniques, so that the relative merits of each method can be maximized in order tominimize operator workload and minimize performance. If properly instrumented the results of themeasurement process should be displayed to operators in order to keep them informed of the state of theinvocation process.

2.3

Experimentation

The adaptive automation process is more complex than simply unloading (or engaging) the operator of a task,irrespective of the invocation process. To be effective, the invocation process must be sensitive to theoperator’s combined tasking environment, which depends on interactions among tasks as well as overallworkload, stress, and safety considerations [36]. The effectiveness of automation is examined by looking atits impact on human performance, mental workload, and situation awareness [22].

To gain an understanding of the operator constraints in the supervision of robotic assets and adaptiveautomation, the Army Research Laboratory and George Mason University developed a simulation test bed,Robotic NCO(Figure 3).Robotic NCOsimulation isolates the cognitive requirements of just the tasks inwhich robotic assets are involved from the larger military environment. The simulation requires operators tocomplete three military tasks from the same display space: UAV sensor use for target detection, UGVmonitoring, and communications. Operators perform either the UGV or UAV task requiring them to switchbetween the displays using the designated buttons when one task or the other demands their attention. Duringthe simulation, the UAV gets electronic intelligence hits from possible targets, which are displayed in theUAV view as white squares. When the operator sees a target, he

or she zooms in on that location andidentifies the possible target, which is then displayed on the situation awareness (SA) map. At the same timethe UGV moves through the area via pre-planned waypoints. The UGV stops at various times and the UGVstatusbar flashes. The operator then switches to the UGV display. The UGV stops for two reasons; it hasreached a reconnaissance point or an unknown obstacle. The operator either resumes the UGV along its pre-planned path or reroutes the UGV, depending on the reason for the stop. Communications are alsocontinuously received during the simulation. The operator is prompted (both visually and

auditorily) atvarious times for UGV/UAV status update and location of targets. The operator also has to perform acommunications tasks in which he or she hears call signs at random intervals that are either ignored or, if owncall sign, needs to be acknowledged.

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Figure 3.Robotic NCO

Simulation

2.3.1

Effect of Task Difficulty on Performance and Situation Awareness in the Robotic NCOSimulation

Through experimentation with the Robotic NCO simulation we are investigating the effects of controllingmultiple unmanned systems on workload and performance of primary and secondary tasks.

Cosenzo,Parasuraman, Novak, and Barnes [7] had participants conduct a reconnaissance mission using theRoboticNCO

simulation. As with the Rehfeld et al. study [28], active (telerobotic) robotic control was a not a factor,since operators used waypoints to

direct the UGV. The participants used the UAV and UGV to identifyenemy units in the area and to respond to communications. With the information received from the UAV andUGV operators were asked to choose a safe path for a platoon to take through the reconnaissance area.

The first experiment examined the effects of task difficulty on performance, workload, and SA(operationalized according to the Kaber & Endsley [16]) in the Robotic NCO simulation. Three potentialdrivers of task load were manipulated:the number of UAV targets to be identified (low=10 targets, high=20targets), the number of UGV stops and requests for operator assistance (low=5 stops/requests, high=7stops/requests), and finally the uncertainty associated with number of high-priority communications (low=4,high=16, out of a total of 20). 16 out of 20 in the low uncertainty condition, 4 out of 20 in the high uncertaintycondition. During the mission, the communications window also issued SA probe questions to the participant.At the end of each simulated mission, participants were asked to use the information received from the UAVand UGV to choose a safe path for a platoon through the reconnaissance area. In summary, the experimentwas a 2 x 2 x 2 within subjects design with the factors manipulated being the number of UAV targets, thenumber of UGV requests, and the uncertainty of high-priority communications. Multivariate analyses ofvariance and subsequent analyses of variance were conducted to examine the effects of task load onperformance.

Overall, the results showed that participants were good at integrating information received from the UAV andUGV to choose the platoon path. However, performance on the individual tasks was diminished due to themultitasking requirements of theRobotics NCO

simulation. The results in Figure 4 show that participantsgenerally took longer to respond to high-priority communications when they had to also identify many UAVtargets (UAV Targets x UGV Requests x Communication,F

(1,16) = 5.73,p

=.02). Additionally, when theuncertainty of high priority communications was high, participants took longer to respond to communicationsSA MAP with UAVand UGV Path-

Friendly Units

Zoomed In

UAV View

Possible Target

CommunicationsScreen

UGV StatusMenu

UAV StatusMenu

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when they had many UAV targets and UGV requests. This result appears to indicate that high-priority butinfrequently occurring communications pose a particularly high monitoring load on the operator, as suggestedby recent studies of vigilance and monitoring in semi-automated systems [32]. Participants also took longer torespond to UGV obstacles when there were many UAV targets to identify (UAV Targets x UGV Requests,

F(1,15) = 17.96, p= .00), indicating that the combined load of processing both tasks was a major contributor tothe operator workload. Additional results were that when there were fewer targets to be identified with theUAV, overall SA was higher (UAV Targets x UGV Requests x Communications,F

(1,15) = 12.91,p

= .00).In addition, trends in the data showed that comprehension of the situation (Level 2 SA) was better when theuncertainty of communications was low. In general performance and situation awareness was compromisedwhen task load was high (i.e., many UAV targets and UGV requests).

100012001400160018002000Low UGVHigh UGVLow UGVHigh UGVLow UAVHigh UAV

Figure 4. Mean Reaction Time to Low and High-Uncertainty Communications As a Function of UAV andUGV Load.

2.3.2

Effects of Task Difficulty on Change Detection Performance in the Robotic NCO Simulation

In the first experiment we identified the drivers of task load in the Robotic NCO simulation and showed thatthe task difficulty manipulations provided some evidence of performance decrement on individual tasks.Results from the first experiment did not yield evidence formajor

areas of human performance that could bemitigated with automation. One reason, of course, is that theRobotic NCOsimulation already implicitlyincludes considerable automation. For example operators used waypoint control rather than teleoperation forthe UGV task. Also, the operator did not have to plan the paths, so in effect, operators were assisted with anautomated path planner. In addition, the UAV task was largely automated, apart from target identification.Another possibility is that the performance measures (e.g., overall accuracy and speed of UAV targetidentification, etc.) that we used were not sufficiently sensitive to

transient or dynamic changes in workloadand SA that might be better captured with other measures. One such measure ischange detection

performance. People often fail to notice changes in visual displays when they occur at the same time asvarious forms of visual transients [33]. Savage-Knepshield and Martin [31] and Durlach [9] have shown thatthis “change blindness” phenomenon, which has typically been demonstrated for basic laboratory tasks or forstaged real-world activities such as sports or social interaction, also occurs with complex visual displays usedin various military command and control environments. In Experiment 2 a change detection measure wasincluded to assess operator attention allocation during aRobotic NCOmission.

In Experiment 2 wedropped the UGV request manipulation, using only what was the previous high UGVcondition (7 requests) and varied only the number of UAV targets (low=10, or high=20) and the uncertaintyof the high-priority communications (low or high) as before. We embedded a change detection measure intoMean Reaction Time

Low Uncertainty

Communications

High Uncertainty

Communications

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the SA map of theRobotic NCOinterface. At unpredictable times during the simulated mission, and after theSA map had been populated to a degree, an icon on the SA map changed its location. Participants wereinstructed

that such changes might occur and that if they noticed them, to press the space bar. Only a simpledetection response was required, not identification or recognition. On the basis of the extensive changeblindness literature [33], we predicted that change detection performance would be especially poor when avisual transient was present, in the present case, when the UGV stopped and requested assistance from theoperator, in which case the UGV status bar flashed. However, in a complex visual display where many itemscompete for attention, change detection performance may be poor even without such visual transients, due tothe need for attention to be allocated to many different sub-tasks, windows, and display locations. Wetherefore predicted that changedetection accuracy would be low even in the absence of an explicit displaytransient, although not as low as with the UGV flash event. To test this prediction, we also included changeevents during the UAV task, when no explicit visual transient was present. Multivariate analyses of varianceand subsequent analyses of variance were conducted to examine the effects of task load on performance.

Results showed that change detection accuracy was extremely low, ranging from 9% to 44%. Morespecifically, changedetection performance was low in the UAV task (averaging about 35%) but significantlyhigher than in the transient UGV task, averaging about 13% (main effect for UAV Targets,F

< 1.0. Third,change detection in the UAV task was lower when the high-difficulty high uncertainty in the communicationstask

(low number of priority messages) was combined with a high number of UAV targets (See Figure 5). Inother words, UAV target load reduced change detection performance when the operator was also loaded withmonitoring infrequent communications messages.

In summary, these results show that the change detection measure was sensitive to the hypothesized effects,being greater for UGV transients than for changes during non transient events (e.g., the UAV task). Moreover,the measure was sensitive to UAV target

load. Overall the results suggest that the change detection measurecould be used to assess the possible enhancing effect of automation of one of theRobotic NCOtasks, as thereis considerable “room for improvement” with these performance levels.

Figure 5. Mean Number of Changes Detected As a Function of UAV and Communications Uncertainty

Low Uncertainty

Communications

High Uncertainty

Communications

Low Uncertainty

Communications

High Uncertainty

Communications

Low UAV

High UAV

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3.0

CONCLUSIONS

3.1

Future Research

The research with theRobotic NCOsimulation by Cosenzo et al. [6, 7] and the simulation research by Chenand colleagues [5] have identified several tasks to consider for automation or aiding, target identification androbotic control. We are currently evaluating the effects of the various automation methods (i.e., operatorperformance method, modeling method, critical-events method) on operator performance. We are examiningthe influence of model-based, performance-based adaptive automation, and critical events-based automationon operator performance. In the model-based adaptive automation, after a fixed number of missed events anautomatic target recognition system (ATR) is invoked. The participant receives a message indicating thatautomated target recognition (ATR) is being invoked, and that they will no longer be responsible foridentifying targets. The

mission continues as before, with the operator carrying the UGV, communications,and change detection tasks. In the performance-based adaptive automation, when a predefined performancethreshold is met, the ATR is invoked for the UAV task, but not otherwise. In the critical-event basedautomation the automation (i.e., ATR) is invoked based on task difficulty. The follow on studies will comparethe effects of various types of automation on Soldier performance while we vary the multitasking environmentincluding the reliability of the proposed aids [30]. Change detection performance will be used as one measureof the automation’s effectiveness. If the automation does function as predicted and decrease the operators’workload, this should in turn free up resources so that they can attend to other tasks or changes in theirenvironment.

Another important issue we will be evaluating is the question of who is in control of adaptation. Whenadaptive changes are initiated by the system, the result is anadaptivesystem; when implemented by thehuman operator, the system is termedadaptable

[21, 32]. There is a current debate over whether adaptive oradaptable automation is more efficient and acceptable to users [3, 17, 32, 35]. Empirical research will decidethis issue. On the one hand, there is research indicating that adaptive (system-initiated) automation leads tosignificant system and human performance benefits, but that some users may be unwilling to accede to theauthority of the system. On the other hand, Vanderhaegen et al. [35] showed that in an air traffic controlsetting, controllers preferred using a decision aiding tool at times of their choosing (adaptable automation), butthat system performance was better when the system provided the tool during expected times of highworkload (adaptive automation). If these results can be generalized to robotic systems (and it is not clear thatthey can, because so little research has been done), then it poses a dilemma: adaptive systems can boostsystem performance, but operators may not like them, and may disable or turn them off; users may preferadaptable automation, but such a system may not provide optimal benefits. The key to resolving this dilemmamay be in developing adaptable automation that poses only moderate demands on the operator in managingallocation decisions. Miller and Parasuraman [17] have proposed the concept ofdelegation interfaces, inwhich operators have the flexibility to choose different operating points along the adaptable-adaptivecontinuum,

depending on contextual demands, workload, and other factors. Preliminary evidence in support ofthe delegation interface concept was reported by Parasuraman et al. [24] in a simulation study of multi-robotsupervision.

For these reasons, we are in the process of developing an adaptive automation architecture for future armoredvehicles that will control ARVs and monitor UAVs among other important military functions. The objectiveof the architecture will be to partition the multitasking environment intooperator tasks that should remainmanual, tasks that can be fully automated, and those that are candidates for adaptive automation. Based on thearchitecture, we intend to conduct both laboratory research and field exercises with trained operators toinvestigate the performance effects of various adaptive schemes and compare them to fully automated andAdaptive Automation for Robotic Systems

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manual implementations. Performance will include not only performance of the various multitasking functionsincluding remote target with robotic assets but also the ability of the operator to maintain both overall andselective situation awareness.

3.2

General Conclusions

The studies conducted to date have identified some of the major issues and the preliminary results indicatethat adaptive automation may bea useful mitigation strategy to help offset the potential deleterious effects ofhigh cognitive load on Army robotic operators in a multitasking environment. Several important issuesremain. In addition to the identification of candidate tasks for automation, a strategy for implementation foradaptive automation needs to be developed. Under what conditions should automation be used? What is thetrigger for the automation? How much authority should be assigned to the automation versus the operator?Are there reliable physiological triggers practical for our environment? We will frame our adaptivearchitecture around these research issues. We intend to develop increasingly realistic simulations as weunderstand the efficacy of adaptable or adaptive options in

multitasking environments. As a result of ourlaboratory results, we intend to validate promising adaptive candidates using prototype crew stations duringrealistic field exercises. The goal of the program is to allow future combat vehicle operators to conduct remotetargeting with aerial and ground robotic systems in a multitasking, high stress environment while maintainingsufficient combat awareness to ensure their survival.